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Posted to issues@spark.apache.org by "XiaoSen Lee (JIRA)" <ji...@apache.org> on 2016/09/02 19:21:20 UTC
[jira] [Updated] (SPARK-17383) improvement LabelPropagation of
graphx lib
[ https://issues.apache.org/jira/browse/SPARK-17383?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
XiaoSen Lee updated SPARK-17383:
--------------------------------
External issue URL: https://github.com/apache/spark/pull/14940
Description:
In the labelPropagation of graphx lib, node is initialized with a unique
label and at every step each node adopts the label that most of its neighbors currently have, but ignore the label it currently have. I think it is unreasonable, because the labe a node had is also useful. When a node trend to has a stable label, this means there is an association between two iterations, so a node not only affected by its neighbors, but also its current label.
so I change the code, and use both the label of its neighbors and itself.
This iterative process densely connected groups of nodes form a consensus on a unique label to form
communities. But the communities of the LabelPropagation often discontinuous.
Because when the label that most of its neighbors currents have are many,e.g, node "0" has 6 neigbors labed {"1","1","2","2","3","3"},it maybe randomly select a label. in order to get a stable label of communities, and prevent the randomness, so I chose the max lable of node.
was:
In the labelPropagation of graphx lib, node is initialized with a unique
label and at every step each node adopts the label that most of its neighbors currently have, but ignore the label it currently have. I think it is unreasonable, because the labe a node had is also useful. When a node trend to has a stable label, this means there is an association between two iterations, so a node not only affected by its neighbors, but also its current label.
so I change the code, and use both the label of its neighbors and itself.
This iterative process densely connected groups of nodes form a consensus on a unique label to form
communities. But the communities of the LabelPropagation often discontinuous.
Because when the label that most of its neighbors currents have are many,e.g, node "0" has 6 neigbors labed {"1","1","2","2","3","3"},it maybe randomly select a label. in order to get a stable label of communities, and prevent the randomness, so I chose the max lable of node.
> improvement LabelPropagation of graphx lib
> ------------------------------------------
>
> Key: SPARK-17383
> URL: https://issues.apache.org/jira/browse/SPARK-17383
> Project: Spark
> Issue Type: Improvement
> Components: GraphX
> Affects Versions: 2.1.0
> Reporter: XiaoSen Lee
>
> In the labelPropagation of graphx lib, node is initialized with a unique
> label and at every step each node adopts the label that most of its neighbors currently have, but ignore the label it currently have. I think it is unreasonable, because the labe a node had is also useful. When a node trend to has a stable label, this means there is an association between two iterations, so a node not only affected by its neighbors, but also its current label.
> so I change the code, and use both the label of its neighbors and itself.
> This iterative process densely connected groups of nodes form a consensus on a unique label to form
> communities. But the communities of the LabelPropagation often discontinuous.
> Because when the label that most of its neighbors currents have are many,e.g, node "0" has 6 neigbors labed {"1","1","2","2","3","3"},it maybe randomly select a label. in order to get a stable label of communities, and prevent the randomness, so I chose the max lable of node.
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